below marketmost similar roles pay hereabove market
This role pays more than
90%
of similar roles. Most pay
$168,500–$241,906
— the blue band above.
At the midpoint, this role pays about
$270k
versus about
$205k
for comparable roles.
Based on 240 similar postings.
Employer
About Apple Inc
Apple Inc. is a multinational technology company known for designing and manufacturing consumer electronics, software, and online services, including the iPhone, Mac, iPad, and App Store. Industry: Consumer Electronics & Software
Apple Inc currently has
3499 open roles
on FindRole.
Listed pay typically runs
$166,600–$277,600
across 2707 roles with salary data.
The Research Engineer / Scientist joins the Machine Learning and AI team to develop foundation models for intelligent features across the product ecosystem. This role involves driving training data and model evaluation strategies throughout the pre-training, mid-training, and post-training lifecycles. You will build high-throughput systems to acquire and curate petabytes of text, code, and multimodal data while defining evaluation portfolios for reasoning, coding, and agentic workflows. Key responsibilities include leading data ablations, scaling-law studies, and decontamination efforts to ensure high-quality, compliant datasets. You will utilize Python and ML frameworks like PyTorch or JAX to solve complex problems in data provenance, privacy, and model-based judging. The work focuses on balancing data, evaluation, and training priorities to improve model performance for on-device and server-side applications.
Shape the strategy for training data and model evaluation across the entire foundation model lifecycle.
Direct the architecture of high-throughput systems to acquire and process petabytes of multimodal data.
Lead data ablations and scaling-law studies to define and validate data mixture decisions.
Define the evaluation portfolio for reasoning, coding, and multi-turn agentic workflows.
Lead decontamination efforts to protect held-out evaluation sets from data contamination.
Build product-aligned evaluation systems that correlate automated metrics with human judgment.
Set standards for benchmark governance, release gating, and data documentation across teams.
Drive data provenance, licensing, and privacy practices in coordination with legal and security teams.
What we're looking for
PhD and 5+ years or MS and 8+ years of experience in machine learning or data infrastructure, including leading technical work at scale.
Deep machine learning expertise in LLMs or multimodal foundation models, with depth in either evaluation methodology or large-scale training data systems and curation.
Strong fundamentals in statistics and experimental design.
A track record of training data or evaluation work on large models that measurably improved model quality or changed how models are built.
Technical leadership experience setting direction, mentoring senior engineers or researchers, and driving adoption of systems, benchmarks, or standards.
Ability to influence senior leaders and cross-functional partners to turn complex results into clear decisions.
Hands-on proficiency in Python and an ML framework such as PyTorch or JAX.
Python
PyTorch
LLM Pretraining
Distributed Computing
Spark
Ray
Deep Learning
Synthetic Data Generation
Data Curation
Machine Learning Systems
Data Processing Pipelines
Model Training
Data Filtering
Deduplication
Scaling Laws
Large Language Models (LLMs)
Large Multimodal Models (LMMs)
PyTorch
TensorFlow
JAX
Transformers
Mixture-of-Experts (MoE)
Distributed Data Processing
Computer Vision
Natural Language Processing
Synthetic Data Generation
Production-scale Inference
Data Curation
High Performance Computing
Artificial Intelligence
Machine Learning
Computer Vision
Image Processing
Python
Physically Based Rendering
3D Scene Data
Image Generation
Image Restoration
Segmentation
Statistics
Scene Semantic Understanding
Light Transport
Illumination Transfer
Appearance Editing